A hyperspectral fluorescence system for 3D in vivo optical imaging

INSTITUTE OF PHYSICS PUBLISHING Phys. Med. Biol. 51 (2006) 2029–2043

PHYSICS IN MEDICINE AND BIOLOGY

doi:10.1088/0031-9155/51/8/005

A hyperspectral fluorescence system for 3D in vivo optical imaging Guido Zavattini1, Stefania Vecchi1, Gregory Mitchell1, Ulli Weisser1, Richard M Leahy2, Bernd J Pichler1,3, Desmond J Smith4 and Simon R Cherry1 1 Department of Biomedical Engineering, University of California-Davis, One Shields Avenue, Davis, CA 95616, USA 2 Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA 3 Department of Radiology, University of T¨ ubingen, T¨ubingen, Germany 4 Department of Molecular and Medical Pharmacology, UCLA School of Medicine, Los Angeles, CA, USA

E-mail: [email protected]

Received 27 July 2005, in final form 15 December 2005 Published 4 April 2006 Online at stacks.iop.org/PMB/51/2029 Abstract In vivo optical instruments designed for small animal imaging generally measure the integrated light intensity across a broad band of wavelengths, or make measurements at a small number of selected wavelengths, and primarily use any spectral information to characterize and remove autofluorescence. We have developed a flexible hyperspectral imaging instrument to explore the use of spectral information to determine the 3D source location for in vivo fluorescence imaging applications. We hypothesize that the spectral distribution of the emitted fluorescence signal can be used to provide additional information to 3D reconstruction algorithms being developed for optical tomography. To test this hypothesis, we have designed and built an in vivo hyperspectral imaging system, which can acquire data from 400 to 1000 nm with 3 nm spectral resolution and which is flexible enough to allow the testing of a wide range of illumination and detection geometries. It also has the capability to generate a surface contour map of the animal for input into the reconstruction process. In this paper, we present the design of the system, demonstrate the depth dependence of the spectral signal in phantoms and show the ability to reconstruct 3D source locations using the spectral data in a simple phantom. We also characterize the basic performance of the imaging system.

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1. Introduction The ability to image specific genes, molecular targets and molecular pathways in vivo is having a profound impact in preclinical research designed to develop new animal models of human disease and to evaluate new therapeutic and diagnostic strategies. Several imaging modalities, notably PET, MRI and optical imaging, have been adapted to facilitate these studies using targeted or activatable imaging probes that produce signals roughly proportional to the regional molecular abundance or the rate of specific events in molecular pathways (Weissleder and Mahmood 2001, Cherry 2004). The absorbing and scattering properties of tissue in the visible and near-infrared (NIR) spectrum limit applications of optical techniques in humans to superficial tissue, and consequently PET and MRI are arguably the preferred modalities for ultimate clinical application. However, in preclinical animal models such as mice, the shorter path lengths allow sufficient photon flux to reach the surface of the animal (especially in the NIR part of the spectrum ∼650–950 nm) enabling fluorescence and bioluminescence emissions to be detected with good signal-to-noise ratios (Troy et al 2004) and using relatively low-cost instrumentation. Optical imaging is therefore the most commonly used modality for small animal in vivo molecular imaging at the present time (Ntziachristos et al 2005). Optical molecular imaging systems have been developed that utilize both bioluminescence (Rice et al 2001) or fluorescence (Graves et al 2003) signals. Bioluminescent systems typically use the firefly luciferase gene as a reporter (Contag and Bachmann 2002). Oxidation of luciferin, which is injected into the animal, leads to the emission of light at around ∼610 nm and occurs only in cells in which the luciferase enzyme is expressed by the reporter gene. The major attraction of this approach is that although absolute light levels are low, signal is produced only where luciferase is present, leading to extremely low background signals (Troy et al 2004). In contrast, fluorescence-based imaging requires an external light source to stimulate the emission of light from the probe. Fluorescence imaging usually results in larger signals, but they are also contaminated by autofluorescence throughout the animal. There are several factors that also make the use of fluorescent probes an appealing approach for molecular optical imaging: (i) fluorescent reporter proteins are available for imaging cell trafficking, infection, gene delivery and gene expression in transgenic animals (Campbell et al 2002), and fluorescent contrast agents are available for receptor imaging and for molecular imaging of cellular events using locally activatable ‘smart probes’ (Mahmood et al 1999); (ii) signal strength is larger than in bioluminescent systems allowing the use of less expensive cameras and (iii) results are readily cross-validated with traditional ex vivo methods using fluorescence microscopy. A typical arrangement for in vivo fluorescence imaging involves placing the animal in a black box, illuminating with a laser or broadband source and imaging with a CCD camera. Excitation and fluorescent light are differentiated through the use of frequency selective filters. The resulting data are a 2D map of the fluorescence intensity at the surface of the animal. While this leads to simple, high-throughput imaging, in which fluorescent signals emanating from small volumes of tissue can clearly be detected, these surface maps provide limited information on source location and make quantification of the source strength and data interpretation difficult in all but the simplest models (e.g., tumour xenograft models where the likely signal location is known a priori). Meaningful longitudinal studies are also hampered by the need to precisely reposition the animal so that the same 2D projection is collected each time. For these reasons, there is tremendous interest in developing truly 3D tomographic optical imaging approaches for in vivo small animal imaging, in which the location and amplitude of fluorescent signals can be determined and more complex spatial distributions of fluorescent

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sources within the animal can be unfolded. These problems can be directly addressed through development of a 3D tomographic system that uses the light collected from the animal to reconstruct the 3D fluorophore distribution. Despite the complexities of tissue-specific absorption and scattering properties, and the fact that photons typically undergo several hundred scatters before leaving the animal (Yao and Wang 1999), significant progress has been made in developing instrumentation and reconstruction algorithms, both for 3D fluorescence imaging e.g. (Ntziachristos and Weissleder 2001, Graves et al 2003, Chen et al 2000) and for 3D bioluminescence imaging (Chaudhari et al 2005, Gu et al 2004, Wang et al 2004). Nonetheless, accurate reconstructions have generally been limited to fairly simple cases. It is likely that further improvements can be made by introducing additional information into the reconstruction problem. Two obvious opportunities are to use temporal (frequency domain) modulation of the excitation light in fluorescence imaging e.g. (Roy and Sevick-Muraca 2001, Godavarty et al 2003), or, as we are proposing in this paper, to use spectral information to aid in 3D localization (Zavattini et al 2003, Swartling et al 2005) and tomographic reconstruction. We hypothesize that introducing spectral information can significantly aid in 3D fluorescence imaging in three ways: (1) for relatively broad emitters such as traditional fluorophores and fluorescent proteins, spectral information provides information on source depth due to the strong wavelength dependence on absorption and scattering properties of tissue. Generally, shorter wavelengths will be much more readily absorbed than longer (nearinfrared) wavelengths, hence spectra are altered based on the amount of tissue light must pass through to reach the surface. (2) Using narrow emitters such as quantum dots (Gao et al 2004), spectral information may allow highly multiplexed in vivo imaging; (3) as others have shown for 2D in vivo imaging applications, spectral information is important in effectively removing signal components arising from tissue autofluorescence (Levenson and Hoyt 2000). In this paper, we present the development of an in vivo hyperspectral fluorescence imaging instrument and show that spectral information can be used for depth discrimination. By combining data from this instrument with appropriate reconstruction algorithms (that model mouse geometry, tissue-dependent optical properties as a function of wavelength, distribution of excitation light within the animal and detection of emission light), we believe that we can ultimately improve the reconstruction of 3D distributions of fluorescent reporter genes or contrast agents. A number of other groups have investigated biomedical applications of hyperspectral imaging (Ford et al 2001, Levenson and Hoyt 2000, Schultz et al 2001). Most of this work considers 2D spatial data. While Ford et al (2001) describe a tomographic application, reconstruction is restricted to a 2D focal plane. This paper focuses on the development of a flexible instrumentation platform that can provide data to these algorithms, on the characterization of this imaging system and on proof-of-principle demonstration that spectral changes can be related to depth information in phantoms and in animal studies. The formulation of the reconstruction algorithms to provide 3D reconstructions of these data and validation of these approaches are presented separately (Chaudhari et al 2005). 2. Description of hyperspectral imaging system A prototype hyperspectral imaging (HI) system has been developed that enables us to flexibly acquire hyperspectral fluorescence data from phantoms and mice. A schematic and photograph of the system is shown in figure 1. The object of interest is placed on an imaging stage, which can be heated for in vivo mouse studies to keep the animal warm while under anaesthesia. The stage is made from matte black Delron to minimize light reflections and autofluorescence from

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Figure 1. Schematic (top) and photograph (bottom) of prototype hyperspectral optical tomography system.

the stage itself. The object is illuminated from underneath the stage (which has a grid of holes drilled through it for this purpose) using a 640 nm, 4.5 mW laser diode (S2011, Thorlabs Inc., Newton, NJ, USA). An interferometric filter (Andover Corp., Salem, NH, USA) is placed in front of the laser to reduce its linewidth. The system is also designed to accommodate a 7 mW, 532 nm Nd:YAG frequency doubled laser, however all fluorescence measurements reported below utilized the 640 nm laser. The illuminating laser is mounted on an x–y translation stage with 1 mm/revolution screws controlled by 400 steps/revolution stepper motors allowing precise control of the illumination location. The fluorescence emission light emitted at the top surface of the animal is focused with a 17 mm focal length, f/1.4 objective lens (Schneider Optics, Hauppauge, NY, USA) onto the entrance slit of the imaging spectrograph. This spectrograph (Imspector V10E, Specim, Oulu, Finland) images a line across the object and splits the light into its component wavelengths producing an image on the surface of the CCD chip that encodes one spatial dimension (y in figure 1) and the spectral dimension λ. The second spatial dimension (x) is acquired in subsequent frames through relative motion of the spectrograph in the x-axis using a translation stage to create a 3D (x, y, λ) dataset for each illumination position. The CCD camera is a front illuminated 512 × 512 chip (Roper Scientific, Trenton NJ, USA), with 24 µm pixels, thermoelectrically cooled to −40 ◦ C to reduce dark current noise. A front

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Figure 2. Projection of a line pattern onto imaging stage (top) and onto mouse (bottom). The deformation of the lines enables the surface contours of the mouse to be calculated. The object on the right is the nose cone for delivering anaesthetic gas.

illuminated CCD was chosen to avoid etaloning effects present for wavelengths >750 nm in back thinned CCD sensors. Etaloning is created by interference of the light from reflections at the chip surface and leads to position-dependent effects on the intensity at different wavelengths due to small variations in the thickness of the silicon across the CCD sensor. The spectral resolution of the system is governed by the slit width of the spectrograph. In our set-up, we use a slit width of 25 µm, leading to a spectral resolution of ∼3 nm. The spectral acceptance of the spectrograph is from 400 to 1000 nm. The slit length is 14.3 mm, and the spectrograph has a numerical aperture of f/2.4. To prevent excitation light from saturating the CCD, a 2-line (532 nm and 640 nm) interferometric rugate notch filter (Barr Associates, Westford, MA, USA) with ∼10 nm bands centred at 532 nm and 640 nm (corresponding to the emission of the two lasers) is placed in front of the spectrograph. The entire system is built on an optical bench placed inside a light-tight enclosure. The enclosure has internal power connections, and a pass-through for data cables and anaesthetic gas. The 3D volume (x, y, λ) can be acquired for different illumination positions by moving the excitation laser and repeating the acquisition procedure. Software control of the camera settings and automated timing of CCD camera frames with movement of the translation stages are achieved using WinSpec (Roper Scientific, Trenton, NJ). The translation stages are driven by VMX controllers (Velmex Inc., NY). Without any significant effort at this stage to optimize the efficiency of data acquisition, approximately 1 h is required to acquire 3D (x, y, λ) data volumes that provide information on the light emitted from the top surface of the mouse for 40 different illumination (excitation) locations. Reductions in the acquisition time to just a few minutes can likely be realized by faster scanning and simultaneous illumination at multiple locations across the body. The HI system also incorporates a blue LED (as shown in figure 1) that projects a structured light pattern onto the surface of the object being imaged. Distortion of the pattern (figure 2) by variations in the height of the object will allow the surface contours of the object to be calculated for use in tomographic reconstruction (Takeda and Mutoh 1983, Rice et al 2003). Because of the short wavelength of the LED light, these data can be acquired simultaneously

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with the fluorescence data, which may be useful for detecting any animal motion during scanning. 3. Experimental methods 3.1. System linearity and calibration The linearity of the HI system in terms of wavelength, spatial coordinates and signal intensity was evaluated. To determine the spectral linearity and calibrate the wavelength scale, the known line emissions from mercury and argon gas in a standard fluorescent lamp were used to illuminate the objective lens of the imaging system. The location of these discrete spectral lines in the CCD image was plotted against the known wavelength of the emission lines to determine the wavelength calibration of the system. Each line in the acquired spectrum was fit with a Gaussian function plus a background component to determine its location in the CCD coordinate space. The calibration curve was fit by linear regression to determine the linearity of the calibration. A white piece of paper with regularly spaced black lines was also imaged to determine spatial linearity of the HI system signal. To study signal intensity as a function of fluorochrome concentration, two nominally identical dilution series of Vybrant DiD fluorescent dye (Molecular Probes Inc., Eugene, OR) were prepared in ethanol. The solutions, mixed in clear plastic microtubes, and all of final volume 190 µl, had the following concentrations: 10 µM, 500 nM, 25 nM, 1.25 nM, 62.5 pM, 3.1 pM and 0 M (a solution containing just ethanol). Each set of seven solutions was directly illuminated by the laser diode and measured twice, where in each instance three consecutive 5 s exposures of the centre of the sample tube were acquired. Thus at each concentration, four values (two solutions, for each solution two values, each an average of three individual exposures) for signal intensity were obtained. For the 10 µM solution a 0.5 s exposure was used to avoid saturation of the CCD pixels, and the results were multiplied by a factor of 10. From each image, a region of interest was defined to include wavelengths from 690 nm to 810 nm, and in space over the vertical ∼5 mm of volume of solution. The lower limit in the wavelength region of interest avoids including some directly scattered laser light that was not eliminated by the notch filter. The data were background subtracted (empty microtube) with background chosen to be one count per pixel less than the minimum measured value in order to allow plotting on a log scale. 3.2. Spectral sensitivity The combined spectral efficiency of the imaging system (including notch filter, lens, spectrograph and CCD) was measured by illuminating the objective lens with light from a calibrated monochromator and measuring signal intensity in the CCD camera as a function of wavelength. Using the manufacturer-supplied efficiency curve for the monochromator (range 400–1000 nm), the spectral efficiency of the hyperspectral imaging system can be estimated. 3.3. Spectral dependence on source depth in biological tissue Preliminary experiments were performed with the HI system to characterize the spectral variation of the detected fluorescent light as a function of thickness of tissue traversed, as this will ultimately be combined with the intensity of the detected light as the basis for reconstructing source location in vivo. Four slices of muscle (red meat) were used, each about 5 mm thick, and fluorescent point sources containing small quantities of Vybrant DiD dye

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Figure 3. Schematic diagram showing the location and depth of the five fluorescent samples placed within the muscle phantom.

(Molecular Probes, Eugene, OR, USA) with a peak excitation of 644 nm and peak emission at 665 were placed between the slices at different depths. The total thickness of the muscle phantom of 20 mm roughly equals the thickness of a mouse. Slices of red meat were chosen to best approximate the complex spectral absorption and scattering characteristics of real tissue, while still providing a controlled laboratory environment for source placement and measurement geometry. Two sets of measurements were taken, one with the Vybrant DiD fluorescence samples in place and other without. This second measurement was taken to measure the autofluorescence of the muscle sample which turned out to be negligible at an excitation wavelength of 640 nm. The location of the five fluorescent samples is shown in figure 3. The slices of muscle were placed on a flat stage, illuminated from underneath and imaged from above. The illumination was performed on a 9 × 5 grid of points, each 1 cm apart. For each illumination point, the spectrograph was scanned across the meat phantom in 1 mm steps for a total distance of 4 cm. For each position, a 512 spatial bins × 120 spectral bins image was acquired. The spectral bins corresponded to a band from 621.3 nm to 898.4 nm. In this way, a dataset of 9 × 5 hyperspectral images each consisting of 512 × 40 spatial bins × 120 spectral bins was acquired. The set-up used to acquire these data is shown in figure 1. Spectra were extracted for each of the sources and illumination positions, and the spectral shape compared for different source depths. 3.4. In vivo imaging study A simple proof-of-principle experiment was performed to demonstrate that depth-related spectral variations are also seen in vivo and to establish the feasibility of in vivo imaging with the HI system. Nude mice were injected subcutaneously with 5 × 106 PC3 cells overexpressing the antigen NCA on one flank and 5 × 106 wild-type PC3 cells on the opposing flank. The wild-type PC3 tumour acted as an internal control for non-specific binding effects. Tumours were allowed to grow to a target volume of 0.2 cc. Anti-NCA was labelled with Alexafluor 647 dye (Molecular Probes Inc.) at a dye:protein ratio of 6. The excitation maximum of Alexafluor 647 occurs at 652 nm and the emission maximum occurs at 669 nm. 100 µl of 2.24 mg ml−1 labelled antibody (equivalent to a dose of 9 mg kg−1) was injected i.v. into each mouse. Mice were imaged using the hyperspectral imaging system at 2, 3, 4 and 5 days

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(a)

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Figure 4. (a) Image from hyperspectral imaging system of regularly spaced pattern of black lines on a white piece of paper illuminated by fluorescent lamp. Line emissions from Ar and Hg in the lamp are clearly seen. Wavelength calibration achieved using data shown in part (b). (b) Plot of emission wavelengths of Ar and Hg lines from a fluorescent lamp versus channel number in the CCD image demonstrating the linearity of the spectral information (r = 1.0).

post-injection. For imaging, mice were anaesthetized using 1–2% isoflurane. Hyperspectral (x, y, λ) data were acquired at 32 illumination positions, with excitation at 640 nm (80% of the maximum excitation for Alexafluor 647) on an 8 × 4 grid, with 5 mm separation between illumination positions in x and y. At each illumination position, data were acquired for 1 s for each of 40 positions in the y-axis, with a 1 mm sampling distance, thus producing an (x, y, λ) data volume covering the entire mouse body for each illumination position on the surface of the mouse. Total acquisition time was approximately 45 min. 4. Results 4.1. System linearity and calibration An image reflecting the data produced from the HI system is shown in figure 4(a). This shows the result of imaging a pattern of black lines on white paper under fluorescent room lights, displaying spatial information on one axis and wavelength information on the other axis. The discrete emission lines from Ar and Hg in the fluorescent lamp are clearly visualized. When the locations of these lines in the CCD image are fitted against their known wavelengths, linear regression yields a regression coefficient of r = 1.0 (figure 4(b)). The spatial linearity also exhibits excellent linearity (regression coefficient of r = 1.0) demonstrating that there are no major spatial distortions introduced by the optics. Figure 5 shows the signal intensity from the HI system as a function of the concentration of fluorescent dye. Note that the plot has logarithmic scales on both axes, and the points plotted at seven locations on the x-axis correspond to the seven solutions in each dilution series. The solution with no dye is plotted at the extreme left of the figure and is labelled ‘no dye’. At each concentration there are four points, corresponding to the two measurements of each of the two series. The plot shows the average pixel value from the region of interest across each of the three replicate measurements. The line on the graph has a slope of 1 and is the best fit to the data from concentration of 10 µM to 62.5 pM. The response of the system as used here is linear over four orders of magnitude in dye concentration in ethanol, with a sensitivity down to below 1 nM. The solution of 10 µM concentration was visibly blue, suggesting that

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Figure 5. Log–log plot showing measured signal intensity from HI system (integrated from 690 to 810 nm) as a function of fluorophore concentration. At each concentration there are four points, corresponding to two measurements of each of the two dilution series. The points are slightly offset for clarity. The line on the graph has a slope of 1 and is the best fit to the data between 62.5 pM and 10 µM. 14

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Figure 6. Efficiency of the imaging system (notch filter, lens, spectrograph and CCD) as a function of wavelength. The notch filter is designed to block light at 532 and 640 nm, the excitation wavelengths of the two lasers available in the system to illuminate the sample.

there is likely sufficient dye to lead to quenching or other nonlinear effects. The response of the system to solutions of the dye in water was similarly linear (data not shown). This particular dye yields a factor of 40 less light for solution in water as compared to ethanol at a given concentration, so the sensitivity for solutions in water only extend down to 10 nM concentrations. 4.2. Spectral sensitivity The spectral sensitivity of the HI system is shown in figure 6. The sensitivity is determined by a combination of the spectral sensitivity of the CCD camera, the transmission properties of the optical system and filters, and the efficiency of the diffraction grating in the spectrograph. The location of the notch filter can be clearly seen. Although these interferometric filters do not completely eliminate light at the excitation wavelengths of the lasers in the HI system, the attenuation is sufficient to avoid any saturation effects in the CCD, and any remaining

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Figure 7. Spectra from fluorescent sources at different depths within the muscle phantom. All spectra are normalized to the integrated intensity above 710 nm to allow comparison of spectral shapes. The spectra show depth-dependent attenuation of fluorescent light in the wavelength range 650–710 nm.

excitation light is simply removed by wavelength selection within the hyperspectral data. Note that efficiency of the HI system is highest across the wavelength range 650–850 nm, which is the spectral region of most interest for in vivo studies. 4.3. Spectral dependence on source depth in tissue Figure 7 shows the variation in the detected spectrum with source depth for a fluorescent point source in a 2 cm thick piece of muscle. These spectra are the raw intensities and are not corrected for the system efficiency shown in figure 6. For each point source, the spectrum from the pixel with the highest intensity is shown. Because attenuation of the light signal is highly depth dependent, the spectra are normalized to the integrated signal intensity above 710 nm to allow their shapes to be compared on the same scale. In practice, of course, one would make use of both intensity and spectral information. The division of the spectrum at 710 nm into ‘long’ and ‘short’ wavelength bands is arbitrary and is simply chosen to provide a ratio between the two wavelength bands that varies with depth, and in which there is measurable signal in both bands for the range of depths of interest. Using this simple approach of normalizing data in the ‘long’ wavelength band, depth-dependent spectral changes in the ‘short’ wavelength band can clearly be demonstrated. The data are well described by a simple exponential function R = A0 + A exp(−τ r)

(1)

where R is the ratio of the ‘long’ and the ‘short’ wavelength signals as defined in figure 7, r is the distance from the source to the point on the surface from which the measurement is obtained, A0, A and τ are constants, where τ is related to tissue attenuation (but is not the true attenuation, as the signal is normalized to the integrated intensity for wavelengths >710 nm). The results of this fitting procedure are shown in figure 8. This calibration can then be used to reconstruct the position of the sources in the muscle phantom. Defining the surface from which the measurement is made as being at z = 0, the

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ratio R

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Figure 8. Plot of ratio R (see the text for details) as a function of source depth. The observed data are well described by an exponential fit.

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Figure 9. Plot of the reconstructed depth (average of five illumination positions, ±1 s.d.) versus actual depth for the fluorescent point emitters embedded in muscle in the geometry shown in figure 3. The reconstructed depth of the five fluorescent sources are obtained solely using the spectral information and the depth calibration shown in figure 9. The five measurements for each source location correspond to measurements from the five laser illumination locations (±1 cm in x and y directions) closest to the source.

distance from a source at location (x0, y0, z0) to a measurement point on the surface (x, y, 0) is given by
r = (x − x0 )2 + (y − y0 )2 + (z0 )2 . (2) The x and y locations (x0, y0) are determined from the brightest pixel in the hyperspectral data, and using the calibration in figure 8, z0 is computed for each of the sources. The 3D source location is calculated for each of the five (x, y) excitation positions (±1 cm) closest to the source. Beyond this distance, there is no significant fluorescence from the source. The results are shown in figure 9. Despite the significant variations in r for the five different excitation locations of the same source, and the fact that only one excitation location was used to

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Figure 10. Complete dataset from an in vivo hyperspectral imaging experiment. The image has been integrated over the wavelength range 656–748 nm for ease of display. Each mouse image represents a different illumination location on an 8 × 4 grid with 5 mm spacing between illumination points. A structured light source illuminates the mouse for contour determination as described in figure 2. The NCA positive tumour can be clearly seen when the illumination is directly underneath it in the bottom right-hand corner. There are some edge effects and artefacts when the illumination comes close to the edge of the mouse as seen on the top row and left column.

determine the calibration data (the excitation location closest to the source), the reconstructed locations for different illumination positions are in very good agreement. The sources are also reconstructed at approximately the correct depth. There is higher variability for sources far from the excitation source, presumably because the detected signal is much lower and more subject to camera noise. These data are a proof-of-principle demonstration of the use of spectral information (crudely separated into just two wavelength bands) to reconstruct the 3D location of a fluorescent point emitter within biological tissue, albeit, tissue that has uniform optical properties and a simple slab geometry. 4.4. In vivo imaging study The complete hyperspectral dataset, with the fluorescence intensity integrated across the wavelength range 656–748 nm, is shown in figure 10 for a mouse imaged 5 days after administration of the fluorescently labelled antibody. Each image corresponds to a different position for the excitation light source. The striped pattern on the mouse is from the structured line pattern illuminated with a blue LED and will ultimately be used to determine the mouse contours (Takeda and Mutoh 1983, Rice et al 2003) for use in the reconstruction algorithm. Figure 11 shows spectra (normalized to intensities above 730 nm) for fluorescence coming from different locations across the animal. Although the antibody localizes in the antigenpositive tumour, there is still some non-specifically localized antibody in the blood and tissues throughout the body. These spectra are the raw intensities and are not corrected for the system efficiency shown in figure 6. The spectra show similar variations to those observed in the muscle phantom shown in figure 7 and demonstrate that spectral changes can be detected in vivo, although their exact

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Intensity [a.u.]

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Figure 11. In vivo hyperspectral data for signal from a fluorescently labelled antibody acquired x days after injection into a mouse. Top image shows integrated (656–748 nm) spectral images of the mouse viewed for four different illumination positions. At each pixel in each image an entire spectrum is collected; sample spectra (normalized for wavelengths >730 nm) for various regions with antibody signal are shown below the figure. Note the significant variations in spectra at different locations that closely resemble the depth-dependent data shown in figure 8 for the muscle phantom.

relationship to source depth in a non-homogeneous optical medium must still be investigated. The data shown in figure 10 (without the collapse of the spectral data required to present it in the figure) form the input data for the 3D reconstruction algorithm that is currently under development and that incorporates the mouse surface geometry combined with tissue optical properties applied to a segmented anatomical MR or CT image of the mouse (Chaudhari et al 2005). 5. Conclusions A hyperspectral imaging system has been assembled and images have been acquired demonstrating the depth dependence of spectral information both in phantoms and in vivo. In a homogeneous optical medium, we have shown that it is possible to reconstruct fluorescent point

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source locations in 3D using only relative differences in the spectral intensities of the emitted light, rather than the integrated light signal that has been used by others. We are currently working to incorporate both spectral and intensity information into a 3D reconstruction algorithm for bioluminescence (Chaudhari et al 2005) and fluorescence imaging. Future studies will include a detailed evaluation of optimal excitation patterns for fluorescence imaging, simultaneous collection of multiple views of the surface of the mouse and more rapid data collection. Acknowledgments The authors would like to thank Jed Ross and Mary Cole of Genentech Inc. for help in designing the animal experiments and for providing the fluorescent antibody. We also thank Stephen Rendig for help in carrying out the animal studies and Julie Sutcliffe-Goulden and Sven Hausner for assistance in accurate sample preparation. References Campbell R E, Tour O, Palmer A E, Steinbach P A, Baird G S, Zacharias D A and Tsien R Y 2002 A monomeric red fluorescent protein Proc. Natl Acad. Sci. 99 7877–82 Chaudhari A J, Darvas F, Bading J R, Moats R A, Smith D J, Cherry S R and Leahy R M 2005 Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging Phys. Med. Biol. 50 5421–41 Chen K, Perelman L T, Zhang Q G, Dasari R R and Feld M S 2000 Optical computed tomography in a turbid medium using early arriving photons J. Biomed. Opt. 5 144–54 Cherry S R 2004 In vivo molecular and genomic imaging: new challenges for imaging physics Phys. Med. Biol. 49 R13–48 Contag C H and Bachmann M H 2002 Advances in in vivo bioluminescence imaging of gene expression (review) Annu. Rev. Biomed. Eng. 4 235–60 Ford B K, Volin C E, Murphy S M, Lynch R M and Descour M R 2001 Computed tomography-based spectral imaging for fluorescence microscopy Biophys. J. 80 986–93 Gao X, Cui Y, Levenson R M, Chung L W and Nie S 2004 In vivo cancer targeting and imaging with semiconductor quantum dots Nat. Biotechnol. 22 969–76 Godavarty A, Eppstein M J, Zhang C Y, Theru S, Thompson A B, Gurfinkel M and Sevick-Muraca E M 2003 Fluorescence-enhanced optical imaging in large tissue volumes using a gain-modulated ICCD camera Phys. Med. Biol. 48 1701–20 Graves E E, Ripoll J, Weissleder R and Ntziachristos V 2003 A submillimeter resolution fluorescence molecular imaging system for small animal imaging Med. Phys. 30 901–11 Gu X J, Zhang Q H, Larcom L and Jiang H B 2004 Three-dimensional bioluminescence tomography with model-based reconstruction Opt. Express 12 3996–4000 Levenson R M and Hoyt C C 2000 Spectral imaging and microscopy Am. Lab. 32 1–8 Mahmood U, Tung C H, Bogdanov A Jr and Weissleder R 1999 Near-infrared optical imaging of protease activity for tumor detection Radiology 213 866–70 Ntziachristos V, Ripoll J, Wang L H V and Weissleder R 2005 Looking and listening to light: the evolution of whole-body photonic imaging Nat. Biotechnol. 23 313–20 Ntziachristos V and Weissleder R 2001 Experimental three-dimensional fluorescence reconstruction of diffuse media by use of a normalized born approximation Opt. Lett. 26 893–5 Rice B W, Cable M D and Nelson M B 2001 In vivo imaging of light-emitting probes J. Biomed. Opt. 6 432–40 Rice B W, Coquoz O, Kuo C, Nantel N, Nilson D N, Strearns D G, Troy T L, Zwarg D and Cable M D 2003 Abstracts of the 2nd Annual Meeting of the Society for Molecular Imaging (San Francisco, CA) p 209 Roy R and Sevick-Muraca E M 2001 Three-dimensional unconstrained and constrained image-reconstruction techniques applied to fluorescence, frequency-domain photon migration Appl. Opt. 40 2206–15 Schultz R A, Nielsen T, Zavaleta J R, Ruch R, Wyatt R and Garner H R 2001 Hyperspectral imaging: a novel approach for microscopic analysis Cytometry 43 239–47 Swartling J, Svensson J, Bengtsson D, Terike K and Andersson-Engels S 2005 Fluorescence spectra provide information on the depth of fluorescent lesions in tissue Appl. Opt. 44 1934–41

3D hyperspectral fluorescence imaging system

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